Master of Science
Faculty of Computing, Engineering and the Built Environment
School of Computing
Creating the next generation of high-quality practitioners for the IoT industry.
The Internet of Things is expected to have a significant impact on industry with predictions of its success and growth constantly rising.
The MSc Internet of Things Applied Research is a specialist programme that prepares you for a career with skills in Computing Science, Engineering and Data Analytics with a full appreciation for the research / innovation process and how this could be transferred into business should be developed. The course covers leading-edge knowledge of Sensor technology, Networks, Security, Pervasive Computing, Big Data and Data Mining in IoT domain. The course is accredited (initial) by BCS, The Chartered Institute for IT, for Partial CITP (Chartered IT Professional) and Partial CEng (Chartered Engineer).
The delivery of the course is supported by multi-million pound infrastructure of a large-scale pervasive and mobile computing environment, a suite of contemporary sensing technologies and rapid prototyping facilities. The course content has been informed by internationally leading research being conducted by the School of Computing and the School of Engineering and by our strong industry partnerships, most notably with BT through the jointly established £28.6 million BT Ireland Innovation Centre.
The Internet of Things is an exciting and exponentially growing area both within industry and academic. The skills trained from the course are in high demand within the sector across the key verticals of Smart Cities, Industrial IoT, Connected Health and Smart Homes. The course also provides a platform to embark on further research studies.
In this section
September entry: September 2022 — June 2023
January entry: January 2022—September 2023
Each of the taught modules attracts 20 credit points, with the Masters Applied Research Project attracting 120 credit points. Student progression to the Masters Applied Research project is subject to having successfully passed all six modules with the pass mark being 50% for each module. Here is a guide to the subjects studied on this course.
The focus of this module is to provide an opportunity for students to gain an in-depth understanding of pervasive computing and to apply this understanding to a range of application domains through working with wireless sensor networks. The module surveys emerging hardware and software components associated with Pervasive Computing Systems, examining the technical and societal issues concerned with a pervasive infrastructure, wireless networks, protocols and emergent algorithms. In doing so a number of examples of innovative systems and applications are reviewed. The module includes a strong practical element where students will be asked to develop services providing support for wearable and smart home context-aware solutions.
IoT Networks & Security
IoT has emerged as a significant technology that can be used for automation and empowerment. The module covers the life cycle of IoT security mechanisms, including the design, development, management and, most importantly, how they are sustained. The module provides an understanding of the IoT architecture, protocols and security considerations.
Statistical Modelling & Data Mining
This module first provides a systematic understanding of probability and statistics. It then provides an in-depth analysis of the statistical modelling process and how to answer hypothesised questions. Next, the module provides a synthesis of the concepts of data mining and methods of exploring data. The content will be delivered and experienced through lectures, seminars and practical exercises using tools, such as Python, R and Weka. Online tools, such as Blackboard will be used to facilitate blended learning approach. On completing this module, students will be able to compute conditional probabilities and use null hypothesis significance testing to test the significance of results and understand and compute statistical measures such as the p-value for these tests. Students will apply, evaluate and critically appraise this knowledge in a range of complex real-world contexts.
Big Data & Infrastructure
Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases, semantic store and graph stores. Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources. The core concepts of distributed computing will be examined in the context of Hadoop. Students will be taught, practically and theoretically, about the components of Hadoop, workflows, functional programming concepts, use of MapReduce, Spark, Pig, Hive and Sqoop.
Digital Signal Processing
The rapid growth of computer processing especially in embedded systems and, more particularly, with digital signals makes it essential that studies specialising in IoT should acquire a knowledge of digital signal processing methods. Digital signal processing concerns all aspects of the acquisition to processing life-cycle of real world signals. The emergence of low cost and pervasive systems in the form of the IoT provides new opportunities for the embedding of DSP technology. This module enables the student to gain deep understanding and enable them to design, apply, and evaluate digital signal processing techniques as related to IoT.
Embedded Systems & Sensors
An embedded system is an electronic or computer system which performs dedicated control and data access functions in electronics-based systems and applications. Embedded systems play crucial role in modern communications, automotive systems, consumer electronics and medical devices and will provide the foundation for the next generation of smart connected Internet-of-Things (IoT) devices and the digital enterprise. This module covers the most important aspects of the embedded systems and will provide a successful student with theoretical and practical knowledge on the feasibility, reliability, and security of electronic systems, especially those important for existing and future IoT applications.
Masters Applied Research Project
Masters Applied Research Project provides the opportunity for you to demonstrate independence and originality, to plan and organise a large project and to implement this over a sustained period of time. The project will follow a user centred design approach and requires you to identify, define, develop, evaluate and implement a novel solution in a selected application area. This will require you to put into practice the techniques you have been taught throughout the course. With this in mind, the project must be tightly constrained to be achievable, whilst sufficiently open enough for you to demonstrate your ability to engage in innovative approaches towards a contribution to research knowledge.
The applied research project offers an opportunity to deepen your knowledge and develop your skills regarding an area in which you have a special interest. Focusing on this specialised area, the project should investigate all aspects of the user centred design and development process from project definition and ideation through to development, evaluation and implementation. In doing so, a full appreciation for the research / innovation process and how this could be transferred into business should be developed. This will allow you to investigate the pathways to impact from an academic, commercial and social aspect.
The project will undertake an applied piece of research which is novel yet realistic and which builds upon skills and knowledge developed throughout the course. The project will be motivated through a critical evaluation of existing literature and theory and the identification and justification of novel research questions. The project tests the inventiveness, the critical capacities, the project management and the in-depth knowledge and problem-solving skills of the student.
The project must involve design, implementation, experimentation and critical analysis of results, benchmarked against other approaches drawn from the literature. This should follow the user centred design process.
Typically 15 timetabled hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course. Masters Applied Research Project takes place in the final two semesters of the course seperately.
Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.
The course is assessed by 100% coursework.
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until near the start date and may be subject to change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days of attendance will often be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10- or 20-credit modules and postgraduate course typically 15- or 30-credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Masters courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment methods vary and are defined explicitly in each module. Assessment can be via one method or a combination e.g. examination and coursework . Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessment. The precise assessment will depend on the module and may be subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification and the assessment timetable. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have four learning outcomes, and no more than two items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised.
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6 (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Masters degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study. In Masters degrees of more than 200 credit points the final 120 points usually determine the overall grading.
Figures correct for academic year 2019-2020.
Academic staff in the School of Computing are qualified to teach in higher education with most of them holding at least a Postgraduate Certificate in Higher Education Practice. The majority of academic staff in the School (83%) are accredited fellows of the Higher Education Academy (HEA). Within the School of Computing courses are taught by staff who are Professors (20%), Readers/Senior Lecturers (32%) and Lecturers (48%).
The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (20%) or Lecturers (55%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic staff (81%) are accredited fellows of the Higher Education Academy (HEA) by Advanced HE - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures correct for academic year 2021-2022.
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We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
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(i) a second class lower division honours degree or better, in the subject areas of computing, engineering or cognate area from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or
(ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification; and the qualification must be in the subject areas of computing, engineering or related discipline
(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent).
In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme
English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
The entry requirements facilitate accreditation of prior learning.
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The Internet of Things has become one of the most discussed technology trends of recent years, mainly due to the expected impact that it will have and, as a result, how it will change the way people live, work and travel. As the expectations of how IoT will redefine an organisation’s operations grow, so too are the expectations to have knowledgeable and skilled staff in the areas of computing, engineering and data science in addition to having an appreciation for business processes and market potential.
Taking all of this into consideration, graduates from the course will be well placed to progress into a wide variety of careers, across a range of industrial settings within the sector across the key verticals of Smart Cities, Industrial IoT, Connected Health and Smart Homes. We have active Industry engagement and links with vibrant technology sector in Northern Ireland.
The specialist course also offers the opportunity to deepen student knowledge and developing research skills in the area of IoT with the support of the research expertise in the School. Graduates from the course also have opportunity to embark on further research at the Ph.D. level.
The course doesn’t require placement experience.
There are opportunities in the course for you to participate in research and industry related projects in the IoT domain through our two Innovation centres BTIIC and CHIC.
BTIIC is the BT Ireland Innovation Centre (BTIIC) in collaboration with Ulster University and BT. The centre aims to invent new ways of using data analytics, artificial intelligence and the IoT, through two work streams of Intelligent System and IoT.
CHIC is the Connected Health Innovation Centre is funded by Invest NI to support business led research in the area of connected health, with focus on data analytics and IoT. The centre currently has over 30 national and international member companies with both technical expertise and clinical experience.
Visit Tuition Fees 2022/23 for more details on the price of this course.
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See the tuition fees on our student guide for most up to date costs.
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